Brand Personality Inference and Recommendation System

ABSTRACT

Mechanisms are provided to implement a brand personality inference engine. The mechanisms receive crowdsource information and extract features associated with a brand from the crowdsource information. The crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source. The mechanisms analyze features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand. The mechanisms generate an inferred brand personality data structure, representing a perceived brand personality of providers providing the crowdsource information, and output an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for performingbrand personality inference analysis and generating recommendations asto actions to be performed to achieve a desired brand personalityperception based on the brand personality inference analysis.

The term “brand personality,” first introduced by Martineau, “ThePersonality of the Retail Store,” Harvard Business Review, 36, 1958, pp.47-55, refers to a set of human characteristics associated with a brandor trademark. A brand has a personality because people tend to associatehuman attributes with brands. For example, the Apple™ brand isconsidered by many to be a “young” brand while Texas Instruments™ isconsidered by many to be a relatively “old” brand. Within thirty yearsof Martineau's introduction to the concept of brand personality, brandpersonality became widely accepted by both marketing scholars andpractitioners as an effective means of business success. Brandpersonality is a key component of brand performance, such as brandidentification, brand trust, and brand loyalty.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided, in a dataprocessing system comprising a processor and a memory, the memorycomprising instructions which when executed by the processor cause theprocessor to implement a brand personality inference engine. The methodcomprises receiving, by the brand personality inference engine,crowdsource information. The crowdsource information comprises naturallanguage content submitted by a plurality of providers to a crowdsourceinformation source. The method further comprises extracting, by thebrand personality inference engine, features associated with a brandfrom the crowdsource information. Moreover, the method comprisesanalyzing, by the brand personality inference engine, the featuresassociated with the brand in accordance with a brand personality modelconfigured to predict a brand personality for the brand based on thefeatures associated with the brand. In addition, the method comprisesgenerating, by the brand personality inference engine, an inferred brandpersonality data structure representing a perceived brand personality ofproviders providing the crowdsource information. The method alsocomprises outputting, by the brand personality inference engine, anoutput indicating aspects of the perceived brand personality based onthe inferred brand personality data structure.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram illustrating brand personality traitgroupings and corresponding top brands for the various traits;

FIG. 2 is an example diagram of a distributed data processing system inwhich aspects of the illustrative embodiments may be implemented;

FIG. 3 is an example block diagram of a computing device in whichaspects of the illustrative embodiments may be implemented;

FIG. 4A is an example diagram of a brand personality inference system inaccordance with one illustrative embodiment;

FIG. 4B is an example diagram illustrating a portion of regressionresults, using a trained brand personality model and brand personalitypredictor on an example input brand and input crowdsource information;

FIG. 5 is a flowchart outlining an example operation for brandpersonality prediction/inference in accordance with one illustrativeembodiment;

FIG. 6 is flowchart outlining an example operation for generating newtraits for a BPS based on crowdsource feedback in accordance with oneillustrative embodiment;

FIG. 7A is an example block diagram of a brand comparison system inaccordance with one illustrative embodiment;

FIG. 7B is an example diagram of a visual output of a brand comparisonsystem that may be part of a graphical user interface in accordance withone illustrative embodiment;

FIG. 8 is a flowchart outlining an example operation for performingbrand comparisons in accordance with one illustrative embodiment;

FIG. 9 is an example block diagram illustrating a brand personalityperception gap assessment system and brand personality perception gaprecommendation system in accordance with one illustrative embodiment;

FIG. 10 is a flowchart outlining an example operation for performingbrand personality perception gap assessment in accordance with oneillustrative embodiment; and

FIG. 11 is a flowchart outlining an example operation for performingbrand personality perception gap recommendation and action commandgeneration in accordance with one illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for performing brandpersonality inference analysis and mechanisms for generatingrecommendations as to actions to be performed to achieve a desired brandpersonality perception based on the brand personality analysis.Moreover, the mechanisms of the illustrative embodiments provide a brandcomparison engine that identifies gaps between brands as well aspotential competitors/partners based on identified overlap in socialnetworking connections of users of the brands. Because the mechanisms ofthe illustrative embodiments are directed to the analysis of brandpersonality and the use of brand personality as a way to direct actionsto be performed, it is important to first have an understanding of brandpersonality and how brand personality is evaluated and measured. Thus,the following description will begin with an introduction to theconcepts of brand personality and mechanisms for measuring andevaluating brand personality, followed by a description of theoperational elements of the illustrative embodiments that operate toanalyze brand personality and generate recommendations and/or performactions, based on the analyzed brand personality.

The mechanisms of the illustrative embodiments recognize that socialmedia provides a tremendous opportunity to shape the perceivedpersonality of a brand. Despite a large amount of research efforts inconceptualizing brand personality and its contributing factors, littleis known about the relationship between brand personality and socialmedia. The mechanisms of the illustrative embodiments analyze how brandpersonality associates with contributing factors manifested in socialmedia. In some illustrative embodiments, based on the analysis ofthousands of survey responses and a large corpus of social media dataregarding hundreds of brands, importance factors contributing to brandpersonality are quantified to generate a brand personality modeldeveloped based on social media data. This brand personality model isutilized to analyze brands, determine divergent brand personalities fromintended or desired brand personalities, generate recommendations forachieving the intended or desired brand personalities, and initiate, insome cases, the performance of actions to achieve the intended ordesired brand personalities. These mechanisms of the illustrativeembodiments will be described in greater detail hereafter.

Brand Personality

Initially, it should be noted that the term “brand” refers to anydesignation of a product, service, location, or any other designation ofan entity. Brands are often represented by trademarks, service marks,trade dress, or other identifier of a good, service, or source ofgoods/services. In general, the term “brand” will be used in the presentdescription to refer to any designator of an entity to which peopleattribute human personality traits.

As noted above, a brand has a personality because people tend toassociate human attributes with brands. Brands are often sociallysignificant to groups of people such that people equate various emotionsand/or human attributes to the brands which in turn elicits thoseemotions and/or attributes in the persons consuming the entitiesassociated with the brands. For example, products associated with brandsare often consumed in a social setting where a brand's personalitycreates brand differences and satisfies customers' self-expressionneeds. Consider red wine, for example. Few customers can distinguishtaste differences between various red wines. However, wine brands havedifferent personalities and, when served in a social setting, can make apowerful statement about those who drink them. Moreover, a person's viewof the brand's personality can affect the physiological and/orpsychological reaction a person has to the product associated with thebrand, e.g., the wine may taste better to the person because it isassociated with brand A while another wine associated with brand B maybe less pleasing.

A great number of theoretical and empirical studies have been carriedout to measure brand personality and identify its contributing factors.Researchers initially relied on qualitative methods, such asphoto-sorts, free associations, and psycho-dramatic exercises. However,these open-ended techniques are often abandoned in the later stages ofresearch as marketers look for more quantitative ways to detect andenumerate differences among their brands. Also, researchers attempted touse human personality scales developed in psychology to directly measurebrand personality. However, these scales are not adequate and powerfulenough to describe the personality of a brand.

The largest research stream focuses on the validation of various brandpersonality dimensions. The most known brand personality measure wasdeveloped in 1997 by J. L. Aaker and documented in “Dimensions of BrandPersonality,” Journal of Marketing Research, 34, 1997, pp. 347-356. Asdescribed by Aaker, the brand personality scale is comprised of 42traits grouped into five large dimensions: sincerity, excitement,competence, sophistication, and ruggedness. Brand personality scaleshave been demonstrated to be a reliable, valid, and generalizable scalefor assessing brand personality. Since 1997 most marketing literaturehas adopted a Likert scale survey approach based on the Aaker scale toassess brand personality. FIG. 1 illustrates an example table of aportion of the five large dimensions and their corresponding traitsalong with names of the top 5 brands associated with the correspondingtrait and descriptive statistics, as may be determined from a Likertscale survey.

Another category of research into brand personalities examines thefactors affecting perceived brand personality. For example, there may bethree main factors that influence and form brand personality, e.g., UserImagery, Employee Imagery, and Marketing Message, also referred toherein as “principle driving factors”. User Imagery and Employee Imageryare the set of human characteristics associated with typical users andemployees of the brand. Based on stereotyping theory, customers maydevelop generalized beliefs about groups of users/employees in which allindividuals from one group are regarding as having the same set ofleading characteristics. Customer's beliefs about users and employeesmay affect their perceptions of the corresponding brand. Marketingmessage refers to marketing messages which are designed specifically tomake consumers aware of a brand and develop a positive attitude towardsthe brand. Marketing messages are often distributed to consumers througha variety of media channels such as social media, television, radio, andthe like. User Imagery, in theory, is considered to be the primaryfactor driving brand personality.

Surveys are generally used to obtain information about user/customerperception of a brand. However, an inherent limitation of survey-basedapproaches for determining brand personality is the flexibility of asurvey. Conducting a survey is often a time-consuming andlabor-intensive process and thus, it becomes expensive to assess brandpersonality frequently. On the other hand, it has been determined thatbrand personality actually does change frequently, especially as newmarketing messages are generated and broadcast, new products andservices are released, and the like, leading to a need to actuallyevaluate brand personality on a more frequent basis. That is, while ithas been determined that human personality exhibits temporalconsistency, i.e. human personalities do not change over time, brandpersonality does not exhibit the same temporal consistency and thus,human personality scales developed in psychological studies and utilizedin such surveys are inadequate to describe the personality of a brand.Furthermore, survey based approaches suffer non-response and samplingrelated deficits. In addition, survey based approaches can suffer fromadministrator bias in that the person administering the survey orcreating the survey may intentionally or unintentionally introduce biasinto the survey questions themselves.

Moreover, implementing an intended brand personality is a challengingendeavor for any brand owner. In practice, brand managers often have anintended brand personality that they wish to achieve through marketingefforts and devote extensive resources to these marketing activities totry to achieve these brand personalities. However, these marketingactivities often fail to ensure consumers perceive the brand in the waythey intended. There may be many theoretical strategies for implementingan intended brand personality, but these strategies are often generaland do not consider the specific context of the brand. There are nocomputational approaches or systems to assist brand managers indetermining actions to be taken to maximize the probability that anintended brand personality will be achieved.

Overview

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Thus, the illustrative embodiments may be utilized in many differenttypes of data processing environments. In order to provide a context forthe description of the specific elements and functionality of theillustrative embodiments, FIGS. 2 and 3 are provided hereafter asexample environments in which aspects of the illustrative embodimentsmay be implemented. It should be appreciated that FIGS. 2 and 3 are onlyexamples and are not intended to assert or imply any limitation withregard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIG. 2 depicts a pictorial representation of an example distributed dataprocessing system in which aspects of the illustrative embodiments maybe implemented. Distributed data processing system 200 may include anetwork of computers in which aspects of the illustrative embodimentsmay be implemented. The distributed data processing system 200 containsat least one network 202, which is the medium used to providecommunication links between various devices and computers connectedtogether within distributed data processing system 200. The network 202may include connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 204 and server 206 are connected tonetwork 202 along with storage unit 208. In addition, clients 210, 212,and 214 are also connected to network 202. These clients 210, 212, and214 may be, for example, personal computers, network computers, or thelike. In the depicted example, server 204 provides data, such as bootfiles, operating system images, and applications to the clients 210,212, and 214. Clients 210, 212, and 214 are clients to server 204 in thedepicted example. Distributed data processing system 200 may includeadditional servers, clients, and other devices not shown.

In the depicted example, distributed data processing system 200 is theInternet with network 202 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 200 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 2 is intended as anexample, not as an architectural limitation for different embodiments ofthe present invention, and therefore, the particular elements shown inFIG. 2 should not be considered limiting with regard to the environmentsin which the illustrative embodiments of the present invention may beimplemented.

As shown in FIG. 2, one or more of the computing devices, e.g., server204, may be specifically configured to implement a brand personalityassessment and recommendation system 220. The configuring of thecomputing device may comprise the providing of application specifichardware, firmware, or the like to facilitate the performance of theoperations and generation of the outputs described herein with regard tothe illustrative embodiments. The configuring of the computing devicemay also, or alternatively, comprise the providing of softwareapplications stored in one or more storage devices and loaded intomemory of a computing device, such as server 204, for causing one ormore hardware processors of the computing device to execute the softwareapplications that configure the processors to perform the operations andgenerate the outputs described herein with regard to the illustrativeembodiments. Moreover, any combination of application specific hardware,firmware, software applications executed on hardware, or the like, maybe used without departing from the spirit and scope of the illustrativeembodiments.

It should be appreciated that once the computing device is configured inone of these ways, the computing device becomes a specialized computingdevice specifically configured to implement the mechanisms of theillustrative embodiments and is not a general purpose computing device.Moreover, as described hereafter, the implementation of the mechanismsof the illustrative embodiments improves the functionality of thecomputing device and provides a useful and concrete result thatfacilitates brand personality assessment and the generation ofrecommendations for performing actions to improve the perception ofbrand personality by closing the gap between the perceived brandpersonality and the intended brand personality of the brand owner. Insome illustrative embodiments, the brand personality and recommendationsystem 220 may further interface with other computing devices toinitiate operations to perform the recommended actions so as to improvebrand personality perception and close the gap.

It should be appreciated that the mechanisms of the illustrativeembodiments interface with crowdsource computing systems, such as may beprovided by other servers 206, one or more client computing devices210-214, or the like, to obtain the necessary information upon which toassess the brand personality and utilize the assessment to generaterecommendations and perform actions. These crowdsourcing computingsystems may take many different forms including social networkingwebsites hosted by one or more computing devices, news groups, blogs,crowdsourced databases or knowledge bases, or any other source ofinformation authored or originating from multiple users, sources, or anyother type of provider of crowdsource information. The crowdsourcecomputing systems are used to infer brand personality scales for brandsas well as identify new brand personality traits to be tracked both withthe particular brand analyzed and future brands.

The brand personality and recommendation system 220 infers thepersonality of a brand from the perspective of multiple constituents(e.g., users, customers, or the like) that are members of thecrowdsourcing computing system, and combines these perspectives intofeedback regarding the brand's personality ultimately generating brandpersonality scale (BPS), qualitative explanations of the inferred BPS,and newly identified traits to be utilized in evaluating the BPS andfuture BPS of other brands. The brand personality and recommendationsystem 220 may perform such assessments of brand personality based oncrowdsourcing computing system information as an input for a pluralityof brands and compare the resulting BPS generated for each of the brandsto determined similarities and differences in brand personality. Inaddition, analysis is performed to determine overlap of crowdsourcingsources with regard to the compared brands to calculate group metrics. Avisual analytic output may be generated to visualize the relationshipsbetween brands and their corresponding brand personalities includingidentification of competitors, potential partners, and the like.

The brand personality and recommendation system 220 further operates toidentify brand personality perception gaps between the assessed brandpersonality and an intended or desired brand personality. That is, thebrand personality and recommendation system 220 may be provided with anintended brand personality scale (BPS) for a particular brand. Themechanisms of the brand personality and recommendation system 220 mayassess the brand personality using crowdsourcing computing systems asdiscussed previously, or otherwise be provided with perceived brandpersonality scale (BPS) generated from analysis of recent and/orhistorical data. The brand personality and recommendation system 220compares the intended and assessed BPS to determine gaps between theintended and perceived brand personalities. Moreover, the brandpersonality and recommendation system 220 may assess the temporalchanges of perceived personality over time and provide an outputindicative of the determined gaps and temporal changes of intended andperceived brand personality.

Furthermore, the brand personality and recommendation system 220 mayfurther operate to provide recommendations and initiate operations forbridging the brand personality perception gaps identified by the brandpersonality and recommendation system 220. That is, given the brandpersonality gaps determined as mentioned above, a severity of the brandpersonality perception gaps is calculated and possible factorsassociated with these gaps are identified. The identification of thefactors associated with the gaps may comprise the execution ofsimulations using predictive models that predict brand personalityassessments for changes in crowdsourcing input data and determining theamount of change exhibited. Solutions for bridging the perception gapsare then determined based on the amount of change achieved to moreclosely approach the intended or desired brand personality scale. Thesolutions may be ranked based on the severity of the gaps, the relevanceof factors associated with the gaps, the brand's relationship with otherbrands, and the like. These solutions are associated with actions thatbrand managers can execute to modify the perceived brand personality.Multiple solutions may also be combined together to form a compositesolution that the brand manager may undertake. In some embodiments,automatic initiation of operations to implement actions associated withrecommended solutions may be performed.

Thus, as shown in FIG. 2, the brand personality and recommendationsystem 220 comprises a brand personality inference system 230, a brandcomparison system 240, a brand personality perception gap assessmentsystem 250, and a brand personality perception gap recommendation system260. The brand personality inference system 230 comprises logic toperform operations, as described in more detail hereafter, for assessingand inferring the brand personality scale (BPS) of a particular brandfrom various multi-modal sources, such as text, audio, and video,generated by multiple constituents of a brand, such as provided via oneor more crowdsourcing systems, e.g., constituents may be employees ofthe owner of the brand, users of the brand, endorsers of the brand,analysts, of other stakeholder in the brand. The term “stakeholder” asit is used herein is anyone that has a vested interest in the brand andits perception by users/customers.

The brand comparison system 240 comprises logic to perform operations,as described in more detail hereafter, for comparing the brandpersonalities of a given brand with other brands, preferably of asimilar nature, and provide interactive analysis on the similarities anddifferences in brand personality as well as identification of potentialcompetitors and partners. The brand personality perception gapassessment system 250 comprises logic to perform operations, asdescribed in more detail hereafter, for automatically identifying theperception gaps between an intended brand personality and the assessedperceived brand personality at an aggregate level and at a constituentlevel. The brand personality perception gap recommendation system 260comprises logic to perform operations, as described hereafter, toautomatically evaluate the brand personality perception gaps, performsimulation using personality models to identify contributing factors,determine recommended actions to bridge the personality perception gapsbased on the results of the simulation, and in some cases initiateactions to bridge the personality perception gaps.

As noted above, the mechanisms of the illustrative embodiments utilizespecifically configured computing devices, or data processing systems,to perform the operations for brand personality assessment and actionrecommendation. These computing devices, or data processing systems, maycomprise various hardware elements which are specifically configured,either through hardware configuration, software configuration, or acombination of hardware and software configuration, to implement one ormore of the systems/subsystems described herein. FIG. 3 is a blockdiagram of just one example data processing system in which aspects ofthe illustrative embodiments may be implemented. Data processing system300 is an example of a computer, such as server 204 in FIG. 2, in whichcomputer usable code or instructions implementing the processes andaspects of the illustrative embodiments of the present invention may belocated and/or executed so as to achieve the operation, output, andexternal affects of the illustrative embodiments as described herein.

In the depicted example, data processing system 300 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)302 and south bridge and input/output (I/O) controller hub (SB/ICH) 304.Processing unit 306, main memory 308, and graphics processor 310 areconnected to NB/MCH 302. Graphics processor 310 may be connected toNB/MCH 302 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 312 connectsto SB/ICH 304. Audio adapter 316, keyboard and mouse adapter 320, modem322, read only memory (ROM) 324, hard disk drive (HDD) 326, CD-ROM drive330, universal serial bus (USB) ports and other communication ports 332,and PCI/PCIe devices 334 connect to SB/ICH 304 through bus 338 and bus340. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 324 may be, for example, a flashbasic input/output system (BIOS).

HDD 326 and CD-ROM drive 330 connect to SB/ICH 304 through bus 340. HDD326 and CD-ROM drive 330 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 336 may be connected to SB/ICH 304.

An operating system runs on processing unit 306. The operating systemcoordinates and provides control of various components within the dataprocessing system 300 in FIG. 3. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 300.

As a server, data processing system 300 may be, for example, an IBMeServer™ System p computer system, Power™ processor based computersystem, or the like, running the Advanced Interactive Executive (AIX®)operating system or the LINUX® operating system. Data processing system300 may be a symmetric multiprocessor (SMP) system including a pluralityof processors in processing unit 306. Alternatively, a single processorsystem may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 326, and may be loaded into main memory 308 for execution byprocessing unit 306. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 306 using computerusable program code, which may be located in a memory such as, forexample, main memory 308, ROM 324, or in one or more peripheral devices326 and 330, for example.

A bus system, such as bus 338 or bus 340 as shown in FIG. 3, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 322 or network adapter 312 of FIG. 3, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 308, ROM 324, or a cache such as found in NB/MCH 302 in FIG.3.

As mentioned above, in some illustrative embodiments the mechanisms ofthe illustrative embodiments may be implemented as application specifichardware, firmware, or the like, application software stored in astorage device, such as HDD 326 and loaded into memory, such as mainmemory 308, for executed by one or more hardware processors, such asprocessing unit 306, or the like. As such, the computing device shown inFIG. 3 becomes specifically configured to implement the mechanisms ofthe illustrative embodiments and specifically configured to perform theoperations and generate the outputs described hereafter with regard tothe brand personality assessment, comparison, brand personalityperception gap assessment, and brand personality perception gaprecommendation and action initiation.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 2 and 3 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 2 and 3. Also,the processes of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thepresent invention.

Moreover, the data processing system 300 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 300 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 300 may be any known or later developed dataprocessing system without architectural limitation.

As noted above, the illustrative embodiments provide a mechanism forassessing brand personality for various brands, comparing brandpersonalities, identifying brand personality perception gaps, anddetermining recommendations for bridging the brand personalityperception gaps as well as initiating actions to implement suchrecommendations. Each of these functions or operations may be providedin separate systems or subsystems, hosted on the same or differentcomputing devices, such as those described above with regard to FIGS.2-3. These systems, or subsystems, may be integrated into a singleoverall system or may be separate and operate independent of oneanother. That is, in some illustrative embodiments, one system orsubsystem provides input to the next such that all of thesystems/subsystems work together as a whole. In other illustrativeembodiments, each system/subsystem may work independent of the others solong as the proper input is provided, regardless of the particularsource from which that input is received, i.e. whether it be fromanother system/subsystem described herein or another source thatprovides the same type of input. Thus, the following description willset forth the separate systems/subsystems of the illustrativeembodiments in separate sections, however this is not to be construedthat the separate systems/subsystems must be implemented separately ortogether. Any implementation of any one of these, or a combination oftwo or more of these, systems/subsystems is intended to be within thespirit and scope of the present invention.

Brand Personality Inference System

One aspect of some illustrative embodiments of the present invention isthe ability to assess the brand personality of a given brand based on anevaluation of crowdsource information so as to generate inferred brandpersonality scales that indicate the perceived brand personality traitsof a brand. FIG. 4A is an example diagram of a brand personalityinference system for performing such operations in accordance with oneillustrative embodiment. The brand personality inference system 400 inFIG. 4A may be one example of a brand personality inference system suchas system 130 in FIG. 1.

As shown in FIG. 4A, the brand personality inference system 400comprises a controller 410, ground truth data source 420, crowdsourcesources interface 430, a feature engine 440, feature extractionresources 450, brand personality predictor 460, and inferred brandpersonality scales output engine 470. The controller 410 controls theoverall operation of the brand personality inference system 400 andorchestrates the operation of the other elements of the system 400.Operations not specifically attributed to other elements in FIG. 4A areperformed by the controller 410.

The feature engine 440 receives, as input, ground truth data structures420, crowdsource brand personality information from crowdsource sources435 via the crowdsource sources interface 430, and feature extractionknowledge resources 450. Each of these types of input are described ingreater detail hereafter. Based on the inputs received, the featureengine 440 identifies features for brand personality modeling byleveraging brand personality theories relating to the relationshipsbetween perceptions of different personality traits of brands fromvarious consumers of brands. That is, one or more brand personalitymodels 445 are developed through machine learning processes based onnatural language processing of natural language crowdsource informationto extract features that are indicative of brand personality traits andlearning the relationships between these features and these traits so asto generate a predictive model 445 that, when analyzing future naturallanguage crowdsource information regarding a brand, the predictive model445 is able to predict or infer the brand personality traits that groupsof individuals are likely to attribute to the brand.

In some illustrative embodiment, the brand personality models 445comprise a separate model for each of a plurality of traits specified ina Brand Personality Scale (BPS), such as the 42 personality traits inthe Aaker BPS, such that each model has a single dependent variable,i.e., the personality trait, and determines a measure of thatpersonality trait based on the crowdsource information processed for abrand. In some illustrative embodiments, the individual brandpersonality models 445 for each of the personality traits are separateLasso regression models as described hereafter. A collection of resultsof each of the models is used to generate the predicted or inferred BPSfor the brand. In another illustrative embodiment, a single model 445may be utilized that models multiple brand personality traits. Forexample, a multivariate regression technique may be utilized in whichone regression model, having multipole dependent variables, i.e.multiple personality traits, is used.

The ground truth data structure 420 that is received by the featureengine 440 provides a baseline for brand personality modeling in that itprovides a basis by which training of the brand personality model(s) 445is performed. In some illustrative embodiments, the ground truth datastructures 420 are compiled from data collected as part of theconducting of one or more surveys of persons with regard to brandpersonality traits and particular brands. The surveys may beadministered via computer systems and electronic survey mechanisms thatpose questions to users and elicit feedback responses from the users.The surveys may have, for each question, pre-defined potential answersthat are selectable by the user, free-form fields in which the user isfree to enter any textual response that the user wishes to provide, orthe like. With regard to the pre-defined potential answers to thevarious questions, the data for the selected answers may be compiled ina straight-forward manner. With regard to free-form fields, the textentered by the user may be subjected to natural language textualanalysis or the like to extract features indicative of the answer whichmay then be utilized to compile data indicative of the answers to thevarious questions associated with the free-form fields.

In one illustrative embodiment, users that are participants in thesurvey are presented with a standardized electronic questionnaire thatis directed to their perceptions of a brand. The participants rate howdescriptive the various personality traits of a brand personality scale(BPS) are of the brand in question, e.g., using the Aaker BPS, theparticipant rates the 42 traits of the Aaker BPS with regard to how wellthey believe the traits are descriptive of, or are associated with, theparticular brand. Various ranges of scoring with regard to each of thetraits may be provided, e.g., a 0 to 7 scale with 7 being maximallydescriptive and 0 being not descriptive of the brand. The traits may bearranged in random order to control order effects. Duplicative questionsmay be included to filter low quality responses.

In this way, quantitative ratings of various traits in a selected BPSmay be collected for the brand from each of a plurality of users.Moreover, each user, or survey participant, may complete a survey foreach of a plurality of brands. Thus, a plurality of brands haveassociated user provided feedback data regarding how relevant ordescriptive of the brands the users believe the various traits of a BPSare. This data may be aggregated and statistical analysis performed onthe quantitative ratings to generate an aggregate baseline or groundtruth BPS for each of the brands. The crowdsource information from thecrowdsource sources, as described hereafter, may be used as a basis forgenerating a brand personality model which may then be used to generatean inferred BPS which is compared to the ground truth 420 to determinehow well the brand personality model generates results matching theground truth 420. Based on differences between the model's results andthe ground truth 420, adjustments may be made to the brand personalitymodel to cause it to generate more accurate results more closelymatching the ground truth 420, e.g., modifying weight values within themodel, or the like.

The ground truth 420 may be used for feature selection and brandpersonality model 445 training. For example, a decision tree may beestablished for feature selection and the ground truth 420 may be inputalong with raw features extracted from the crowdsource information viathe crowdsource interface 430. Ground truth 420 may be used to train thedecision tree mechanism to determine which features are more importantfor evaluation of particular brand personality traits. For example, afeature located in the upper level of nodes in the decision tree, e.g.,top 3 levels, can be used as relevant features for brand personalitymodeling, such as in brand personality model 445. Thus, crowdsourceinformation and the ground truth 420 may be used together to selectrelevant features that are used to train the brand personality model 445so as to provide an improved training of the brand personality model445.

As discussed above, the ground truth data structures 420 may be obtainedfrom a survey system 425 which administers the surveys electronically toparticipants and collects the participant inputs with regard to brandpersonality traits and then aggregates this data and performsstatistical analysis to generate one or more ground truth datastructures 420 for one or more brands. The ground truth data structures420 may be a baseline BPS for the brand in question, for example. In oneillustrative embodiment, the baseline BPS of the ground truth datastructures 420 for a particular brand is generated by averaging theratings that participants gave for each of the traits of the BPS in theadministered questionnaire of the survey so that the baseline BPScomprises the average ratings, on the established range of ratings, foreach trait in the BPS.

As shown in FIG. 4A, in addition to the ground truth data structures420, the feature engine 440 also receives crowdsource information fromone or more crowdsource sources 435 via the crowdsource system interface430. The crowdsource information may be obtained from accounts (e.g.,user accounts, organization accounts, or the like) associated with thebrand on social networking websites, business networking websites, abrand owner's own computing systems, trade publication source databaseor computing system, or any other corpus of natural language informationthat may be analyzed using natural language processing techniques toextract information indicative of the perception of the brand amongstone or more groups of people. Examples of such crowdsource informationinclude instant messages, text postings to social or business networkingwebsites, articles in journals, electronic mail messages, webpagecontent, or the like.

This crowdsource information may also comprise multi-modal behaviordata, e.g., audio, video, textual, graphical, or the like, representinguser contributions to a networking (social, business, governmental, orother) system which are indicative of the online behavior (contentcontributions) of users categorized into different groups of consumersof a brand. The categorization of users into the different groups ofconsumers may be performed in accordance with the particular crowdsourcesource, for example. That is, if the information is being obtained froma general social networking website, then the crowdsource informationobtained from that website may be categorized as coming from generalusers or general customers of the brand, i.e. customer behavior data. Ifthe information is being obtained from a crowdsource source that isspecific to current employees or former employees of the owner of thebrand, then the information obtained may be categorized as employeebehavior data. If the information is being obtained from a crowdsourcesource that is associated with marketing messages generated by the ownerof the brand or its affiliates, then the information obtained may becategorized as marketing message behavior data or other stakeholderbehavior data.

Various categorizations of crowdsource information may be utilizedwithout departing from the spirit and scope of the illustrativeembodiments. The categorization of crowdsource information may beutilized to evaluate the information differently based on thecategorization when generating a brand prediction model, e.g.,generating different weights values for functions of the brandpersonality model(s) 445, generating different functional relationshipsin the model 445, or the like, as described hereafter. For example, thesources of crowdsource information, or even individual users thatsubmitted the crowdsource information that is then provided by thesource, may be weighted according to inferences generated by computingthe relationship between a user's textual data submitted as theircontribution of crowdsource information and weights associated withkeywords corresponding to a personality trait, where the weightsassociated with keywords may be trained in the brand personality model460.

The crowdsource information, in some illustrative embodiments, iscategorized into three main categories corresponding to the threeprinciple driving factors of brand personality, i.e. employee imageryinformation, user imagery information, and marketing message imageryinformation. Of course other categories of information may also beobtained, such as other stakeholder behavior information and the like,for example, without departing from the spirit and scope of the presentinvention. The crowdsource source interface 430 may be configured tocommunicate with the various crowdsource sources 435 and may beconfigured to categorize the information retrieved from thesecrowdsource sources 435 into the categories previously defined for theparticular crowdsource sources 435. Thus, for example, if it ispreviously determined that information obtained from a Facebook™ socialnetworking webpage associated with a brand is to be categorized as a“user imagery” category of crowdsource information, then the informationgathered from the Facebook™ social networking webpage will be stored ina data structure associated with this category of crowdsourceinformation, or otherwise associated with this category of crowdsourceinformation.

It should be appreciated that this information may be provided in manydifferent modes including audible, textual, and video/graphical modesand may be provided in an unfiltered format. That is, the informationmay be obtained by the feature engine 440 and processed by the featureengine 440 to process the information in accordance with the particularmode of the information, and extract features indicative of brands andthe primary factors contributing to brand personality as well as thetraits of the brand personality scale itself, as discussed in greaterdetail hereafter.

Using the three principle driving factors discussed above, the employeeimagery information comprises behavior data collected from crowdsourcesources that are associated with current/former employees of the ownerof a brand or affiliate of the owner of the brand. These sources mayinclude websites and other social networking systems where current andformer employees can submit their reviews about the owner of the brand,the brand itself, and other information indicative of employee imageryof the brand. Often times, such submissions from current/formeremployees may include statements about working conditions, companyculture, management style, and the like. Such information may becaptured to generate a representation of the employee imagery drivingfactor of brand personality. One example of a crowdsourcing source thatprovides employee imagery category information is the websiteGlassdoor.com which provides a social media platform where current andformer employees can post reviews about their employers.

With regard to user imagery information, this information entails anybehavior data that is associated with customers, consumers, or otherusers that comment on a particular brand and which are notcurrent/former employees of the brand owner or its affiliates, as wouldbe encompassed by the employee imagery information. That is, socialnetworking and other online source of information that are directed tothe opinions and viewpoints of users with regard to brands may beincluded under this category of crowdsource information. For example, abrand's Twitter™ or Facebook™ account, or other social networkingwebsite account, often has followers, friends, or the like. Thesefollowers are likely to be using and/or liking the particular brand inquestion. The set of brand followers may be considered as contributingcrowdsource information that is categorized as user imagery information.In some illustrative embodiments, in order to filter out illegitimateuser accounts on such crowdsourcing sources from legitimate useraccounts, only user accounts that have a description of the user withinthe user account may be used as a source of user imagery informationsince illegitimate user accounts tend to not have any such userdescriptions. Thus, a filtering of the sources may be performed by thecrowdsource sources interface 430 prior to providing the crowdsourceinformation to the feature engine 440.

The marketing message imagery information may be obtained from similarsources to that of the user imagery information and/or employee imagerinformation, but is specifically associated with marketing messages madeby the owner of the brand or its affiliates. For example, a company'sTwitter™ account allows the company to push intended information to thepublic. Such notifications, or “tweets,” from the company, owner of thebrand, or its affiliates, are considered marketing messages. Themarketing message itself, and the reactions and responses to thesemarketing messages that are posted in response to the tweet, may beconsidered as providing crowdsource information categorized as marketingmessage imagery.

The crowdsource information received from the crowdsource sources 435via the crowdsource source interface 430 is processed by the featureengine 440 using natural language processing, image analysis, audiblefeature analysis, or any other content analysis mechanism to performfeature extraction, selection, and new feature construction. A “feature”in this context is any element of crowdsource information that isindicative of a principle factor of brand personality, e.g., userimagery, employee imagery, and marketing message factors. These featuresmay be audible features, textual features, or video/graphical features.For ease of explanation, it will be assumed that the crowdsourceinformation received is in a textual mode and that the “features”extracted, selected, and constructed are textual features, e.g., keyterms, phrases, topics, and the like, which are extracted using naturallanguage processing techniques to analyze the natural language text withregard to both semantic and syntactic features.

The feature engine 440 utilizes feature extraction resources 450 toassist with the feature extraction aspects of its operation whenprocessing the crowdsource information. The feature extraction resources450 provide the resource data for identifying known indicators of theprinciple factors of brand personality, e.g., specific key terms,phrases, topics, patterns of video/graphical image data, and the like.In some illustrative embodiments, the feature extraction resources 450comprise keyword/key phrase dictionaries, synonym dictionaries, and/orany other natural language processing based linguistic and semanticresources used to perform natural language processing on textual inputof the crowdsource information. In one illustrative embodiment, aLinguistic Inquiry and Word Count (LIWC) dictionary may be establishedfor each of a plurality of LIWC categories, e.g., the categoriesgenerally grouped into the groups Linguistic Processes, PsychologicalProcesses, Personal Concerns, and Spoken Categories, that are used tomeasure various ones of the brand personality traits of a selected brandpersonality scale (BPS), e.g., the 42 Aaker brand personality traitsmentioned previously.

In some illustrative embodiments, the LIWC categories correspond tolinguistic and psychological features that may be related to brandpersonality traits. Thus, individual LIWC categories, or combinations ofLIWC categories, may be mapped to the various traits of the particularBPS being utilized, such as by way of a establishing a mapping datastructure. As a result, the calculations and evaluations with regard tothese LIWC categories may be similarly mapped to these traits as well.For example, it may be determined, through machine learning and trainingof the brand personality model(s) 445 using the ground truth 420, that aparticular brand personality trait corresponds to a particular subset ofcategories of the LIWC and thus, the corresponding dictionaries forthose categories may be associated with the brand personality trait. Inthis way, those terms/phrases in the corresponding dictionaries may beused as a basis for evaluating crowdsource information to determineoccurrences of those terms/phrases and utilize the occurrences tomeasure a degree of the brand personality trait being associated with aparticular brand.

The feature extraction operation of the feature engine 440 may operateto extract instances of matching key words, phrases, and the like,matching the terms/phrases in the associated dictionaries correspondingto the brand personality model 445 for the particular brand personalitytraits being evaluated, from the crowdsource information and maintainmetrics associated with the various LIWC categories and/or brandpersonality traits. Thus, each textual statement received in thecrowdsource information may be processed by the feature engine 440 withregard to each of the keyword dictionaries for each of the LIWCcategories to evaluate the statements against the LIWC categories andgenerate counts of instances of keywords in the keyword dictionary forthe particular LIWC category.

Statistical analysis may be applied to the collected counts of instancesof keywords for each of the LIWC categories to generate a plurality ofstatistical descriptors. In one illustrative embodiment, 60 LIWCcategories are utilized with 7 statistical descriptors for each of the60 LIWC categories: mean, 5^(th) to 95^(th) percentile, variance, skew,kurtosis, minimum, and maximum. These statistical descriptors indicatefor each LIWC categories, the most predictive keywords corresponding tothe LIWC category and the degree or confidence of the occurrence ofthese keywords being predictive of the corresponding LIWC category.Thus, a combination of the LIWC categories and statistical descriptorsmay be utilized for each of the principle driving factors of brandpersonality, e.g., user imagery, employee imagery, and marketingmessages, to devise a brand personality model 445 for modeling brandpersonality. For example, assuming 60 LIWC categories, 7 statisticaldescriptors, and 3 principle driving factors, the brand personalitymodel 435 may comprise 1,260 predictive variables which may be evaluatedfor each brand.

Feature selection operations may be performed on these predictivevariables to select variables that are most representative of the eachof the BPS traits. The BPS traits are regarded as dependent variablesthat are dependent upon these predictive variables in accordance withthe established mapping data structures or functions that map theselected predictive variables to the BPS traits. The selection operationmay be performed using, for example, a regression analysis, such as aLeast Absolute Shrinkage and Selection Operator (LASSO) regularizedregression analysis or other machine learning methodology. With a LASSOregularized regression, the selection operation seeks a sparse solutionby shrinking the regression coefficients of weak and/or correlatedpredictors to zero such that the LASSO regression can select a set ofbest explanatory predictive variables.

Moreover, following a standard feature selection procedure, 10-foldcross validation methodology may be used to select the most reliablepredictors and thus, increase the confidence level of selected features,e.g., the coefficient of determination (R²) value as describedhereafter. In one illustrative embodiment, the behavior data is dividedinto 10 subsets, one subset of the data is used as a test set and theremaining 9 subsets are used as training data sets. This generates 10models and each model may select different predictors (predictivevariables) due to different training sets. To ensure reliability ofresults, only predictors that were consistently selected in all of themodels may be utilized in the final brand personality model that is usedfor inferring brand personality scales.

The confidence level of the selected features is determined, in oneillustrative embodiment, by calculating a coefficient of determinationR² of the results of the linear regression or LASSO regularizedregression. The R² value is computed by systematically removing eachsubset, in the 10-fold cross validation, from the 10 subsets of the dataset, estimating the regression equation, and determining how well themodel predicts the removed subset. The calculated R² can avoidoverfitting the brand personality model and can be more useful thanadjusted R² for comparing models because it is calculated usingobservations nod included in model estimation. Larger values of thecomputed R² value suggest a model has greater predictive ability, i.e.has a higher confidence. A R² value may be calculated for each of thetraits of the BPS, i.e. the dependent variables. If the confidencemeasure, e.g., the R² value, meets or exceeds a predetermined thresholdconfidence or R² value, then the corresponding brand personality modelis considered to be sufficiently accurate for predicting brandpersonality and may be output for use during runtime operations topredict or infer brand personality for brands of interest based onground truth and crowdsource information for the brands of interest.

It should be appreciated that the crowdsource information used by thefeature engine 440 may come from various types of consumers includingemployees, customers, and other stakeholders. The feature extraction andselection is performed across all of these consumers so that thefeatures that are extracted and selected for inclusion in the finalbrand personality model are the ones that are most representative of thebrand personality as they are more commonly used by various consumers.

The final brand personality model, or models, 445 is/are implemented ina brand personality predictor 460 during runtime operation. That is, thefeature engine 440 may receive crowdsource information for a brand ofinterest from the crowdsource sources 435 via the interface 430 and mayperform operations to extract features from the crowdsource informationin the manner previously described above. Features may be selected asthose that are most representative of the brand from the viewpoint ofthe crowdsource information and input to the brand personality predictor460 which applies the final brand personality model(s) 445 to the set ofselected features to determine the brand personality scale (BPS) valuesfor the various traits of the BPS based on the configured final brandpersonality model.

FIG. 4B is an example diagram illustrating a portion of regressionresults, using a trained brand personality model 445 and brandpersonality predictor 460 on an example input brand and inputcrowdsource information. The brand personality predictor 460 utilizingthe trained brand personality model 445 uses the three principle drivingfactors of user imagery, employee imagery, and marketing messages todetermine predicted values for the various brand personality traits ofthe brand personality scale. The predicted R² values are reported withregard to each of the personality traits with the predictive variablesand their standardized beta coefficients being shown in the subsequentcolumns in FIG. 4B for each of the three principle driving factors.

The predicted, or inferred, brand personality scale (BPS) trait valuesmay be output along with a qualitative explanation of the predicted orinferred BPS trait values as the inferred brand personality scales 475by the inferred BPS output engine 470. Computational techniques arecombined with crowdsourcing techniques to generate qualitativeexplanations of the inferred BPS trait values. For example, domainheuristics/rules may be utilized to programmatically identifyrepresentative users of a brand on social networks. Then, based on theinferred BPS, the system prompts these users to explain theirperceptions of a brand related to certain traits. These responses areaggregated to explain the inferred BPS trait values. Alternatively, auser's previously submitted content, e.g., posts, instant messages,etc., that reference the brand or otherwise are indicative of the user'sperception of the brand may be used as a basis for generating thequalitative explanations. In some illustrative embodiments, thepersonality traits of the users themselves may be used as the basis forthe qualitative explanation.

For example, the crowdsource information may comprise a plurality ofnatural language textual content submitted by a plurality of users. Thenatural language textual content is tied to the particular users thatprovided the content via an identifier of the user, which in turn may beassociated with a user profile. The user profile may comprisedemographics, a description of the user, etc., which together may beindicative of the type of the user. The profile may be analyzed toassociate personality traits with the user and these personality traitsmay be a basis for generating a qualitative explanation based on acommonality of personality traits associated with representative users.Representative users may be selected as a subset of the users thatprovided natural language textual content which have a highest number ofmatching terms/phrases or other features mentioned in their submittednatural language textual content with the terms/phrases or otherfeatures indicative of the brand personality trait in question, e.g.,the top 10 ranked users. Thus, if a number of representative users havea personality trait of conscientiousness, and the brand personalitytrait in question is trustworthiness, then a qualitative statement as towhy the brand has a relatively high trustworthiness brand personalitytrait value is because the representative users are in generalconscientious.

Moreover, natural language text content that has the most relevance tothe particular brand personality trait, e.g., the most matches ofterms/phrases with those indicative of the brand personality trait, maybe selected as representative qualitative explanations. For example, ifuser A submits an instant message of the type “I love brand A facialtissue because it is soft, durable, and cost effective” and theterms/phrases associated with the brand personality trait in questionare “soft,”, “durable”, “cost”, and “facial tissue”, then this statementby user A may have a relatively large match and be considered aqualitative explanation as to why the brand personality trait value forthe brand personality trait was calculated to be relatively high. Insome instances, however, such statements may not be previously providedby the representative users and thus, a request may be sent to therepresentative users to submit a natural language textual response toexplain their rating of the brand personality trait and their responsemay be used as a basis for the generating the qualitative explanation.Of course, any mechanism for generating a qualitative explanation may beused without departing from the spirit and scope of the illustrativeembodiments.

FIG. 5 is a flowchart outlining an example operation for brandpersonality prediction/inference in accordance with one illustrativeembodiment. The operation outlined in FIG. 5 may be implemented, forexample, by a brand personality assessment systems, such as shown inFIG. 4. It is assumed for purposes of the description of FIG. 5 that afinal brand personality model has already been established through themechanisms described above and the final brand personality model isimplemented by a brand personality predictor to predict the brandpersonality scale (BPS) values for a brand of interest.

As shown in FIG. 5, the operation starts with the receipt, as input, ofone or more ground truth data structures and behavior data, i.e.crowdsource information, regarding the brand of interest (step 510). Forexample, ground truth data structures may be established for the brandand specific crowdsource sources associated with the brand may becontacted to retrieve behavior data, e.g., social networking websiteaccounts associated with the brand, owner of the brand and itsaffiliates, and the like. This information is input to the featureengine which performs feature extraction and selection operations on theinput (step 520) with the selected features being input to the brandpersonality predictor which generates a prediction, or inference, of thevalues of the personality traits associated with a BPS and evaluates theconfidence of the predicted or inferred values (step 530).

The confidence, e.g., R² value, associated with the predicted orinferred trait values is checked against a predetermined threshold todetermine if the result generated by the brand personality predictormeets a minimum threshold level of confidence (step 540). If the minimumthreshold level of confidence is not met, then data sources areidentified and feedback is generated via the crowdsourcing sources andprovided back to the computational operations in steps 520, 530 toperform new predictions, or inferences of BPS for the brand. That is,within step 550, in one illustrative embodiment, the mechanisms of theillustrative embodiments first assign relative weights to a user/sourceof crowdsource information on each individual trait. For instance, theseweights could be inferred by computing the relationship between a user'stextual data from social media and the keyword weights of a personalitytrait produced in the brand personality predictive model 445. Inferredtrait weights of users can be used to identify representative users ofthe brand for each trait. For example, the mechanisms of an illustrativeembodiment may identify a set of brand Twitter™ followers who are highlyrepresentative for the personality trait “rugged” of the brand. Inaddition, qualitative explanations from representative users of a brandare generated. These qualitative explanations can be users' comments onthe brand from social media, responses to questions posed to the usersabout their opinions regarding the brand, or any other suitable mannerof obtaining feedback from the users, for example. In one illustrativeembodiment, if the collected feedback from social media is insufficient,prompts may be generated and output to representative users of apersonality trait, such as via crowdsource sources interface 430 orother communications interface, to describe their perceptions of thebrand on the corresponding personality trait. However, rather thanasking every participant to answer 42 questions of a brand (using abrand personality survey), the mechanisms of the illustrativeembodiments may identify representative users for an individual traitand only asks these identified representative users one correspondingquestion to further elaborate on their opinion of the brand with regardto the particular personality trait that they are associated with.Again, representative users may be identified when analyzing thecrowdsource information which includes identifiers of the usersproviding the content in the crowdsource information such that users whohave submitted portions of the crowdsource information that have arelatively higher number of matches of their submitted content to thefeatures associated with a particular brand personality trait may beselected as representative users for that brand personality trait.

If the minimum threshold level of confidence is met, then the predictedor inferred BPS is output (step 560). In addition, in a paralleloperation, subjects are identified and user/consumer feedback isgenerated (step 570). In two parallel operations, content analysis maybe performed (step 575) in order to identify any new traits (if any) foruse in future BPS generation (step 580). The operation for performingcontent analysis and generating new traits will be described in greaterdetail hereafter with regard to FIG. 6. In addition, from theuser/consumer feedback, qualitative explanations of inferred BPS may begenerated and output (step 590) for use in conjunction with the outputinferred BPS (step 560).

FIG. 6 is flowchart outlining an example operation for generating newtraits for a BPS based on crowdsource feedback in accordance with oneillustrative embodiment. The operation outlined in FIG. 6 may be appliedto the crowdsourcing input received as part of step 570 in FIG. 5, forexample, in order to generate the new traits that are output as part ofstep 580 for example, and returned to the computation operations 520,530.

As shown in FIG. 6, the operation comprises two stages: candidate traitgeneration 690 and candidate trait validation 695. In the first stage690, content analysis techniques, e.g., topic modeling techniques,keyword identification, key phrase identification, pattern matching, orthe like, are applied to discover new traits (step 620) fromrepresentative users' qualitative feedback (received in step 610) of thebrand from the crowdsourcing sources. These traits are added to a poolof candidate brand personality traits as part of step 620. In thecandidate trait validation stage 695, the frequency of occurrence of thecandidate traits across a plurality of brands is calculated and comparedto a threshold frequency (step 630). If the candidate trait frequency ofoccurrence meets or exceeds the threshold frequency (step 640), then thecandidate trait is added to a set of traits for potential addition tothe brand personality model (step 650). Ratings of brands with regard tothe set of potential traits for addition to the brand personality modelare inferred using the mechanisms of the illustrative embodiments (step660). A variance of the inferred ratings on the added traits from thebrands is calculated and compared against a threshold variance (step670). If the variance is less than the threshold variance, then it isconsidered to be a newly identified trait that can be used in the BPSmodel for future BPS value prediction or inference (step 680).

As an example, consider a scenario in which a newly discovered trait isof the type “professional” from user representative feedback. In step620, topic modeling techniques, e.g., Latent Dirichlet allocation, maybe applied to generate new traits, e.g., “professional”, from therepresentative users' qualitative feedback of a brand (obtained in step610). In step 630, the illustrative embodiment counts the frequency ofthe trait “professional” with regard to a database of personality traitsof other brands, e.g., the trait “professional” appears in more than 30%of brands in the database (30% is the threshold in this example). If, instep 640, the frequency count for the trait “professional” appears in30% or more of the brand personality traits of other brands, then instep 650, the illustrative embodiment may adopt a Latent Aspect RatingAnalysis technique to infer the brands' ratings on the candidate trait“professional.”

In step 660, the illustrative embodiment may compute a variance of theinferred ratings on the trait “professional” from the brands. If therating variance of the trait passes a threshold (step 670), it isconsidered to be a newly identified brand personality trait beside theexisting brand personality traits in the BPS (step 680). The threshold,in some illustrative embodiments, may be the media of the variances ofthe existing brand personality traits in the exiting BPS.

Thus, the brand personality inference system 400 provides mechanisms forgenerating a brand personality model and training that brand personalitymodel based on ground truth data, e.g., survey data, and crowdsourceinformation, such as may be obtained from social networking sources(e.g., Facebook™, Twitter™, Instagram™, etc.), business networkingsources (e.g., Glassdoor™, LinkedIn™, etc.), and the like. In someillustrative embodiments, the crowdsource information is naturallanguage text information that is processed using natural languageprocessing mechanisms to extract features, select featuresrepresentative of the particular brand personality traits and/orprinciple driving factors of brand personality, and generate a brandpersonality model using these extracted and selected features.

The brand personality model may then be utilized during runtime topredict or infer a brand personality scale (BPS) representation of abrand's personality comprising a plurality of personality traits andvalues with regard to these personality traits, where the values areindicative of the strength of association of the personality trait withthe particular brand. Moreover, the mechanisms of the illustrativeembodiments are operable to select new brand personality traits that areto be added to the brand personality model for evaluation withsubsequent brands. The output generated may comprise the inferred BPSrepresentation of a brand as well as a qualitative explanation of theinferred BPS which may be provided to a brand manager or otherauthorized user.

Brand Comparison System

The above brand personality inference system mechanisms, e.g., brandpersonality inference system 230 in FIG. 2, for determining a predictedor inferred brand personality may be utilized to generate brandpersonality scales (BPS's) for a plurality of different brands. Thebrand comparison system 240 in FIG. 2 may further be utilized to comparethe BPS for a plurality of brands to identify potential gaps betweenbrands, potential competitors and/or partners for a brand owner or brandmanager, as well as brand relationships, as described hereafter.

FIG. 7A is an example block diagram of a brand comparison system 700 inaccordance with one illustrative embodiment. As shown in FIG. 7A, thebrand comparison system 700 receives as input the brand personalityscales (BPS's) for multiple brands 740. In some illustrativeembodiments, these BPS's 740 may be the inferred BPS's generated usingthe brand personality inference system 400 in FIG. 4, for example. Inother illustrative embodiments, the BPS's 740 may be generated in anyother suitable manner depending upon the implementation. The BPS's 740comprise the BPS for the brands of interest to the comparison and mayinclude additional BPS information for other brands that are notdirectly part of the comparison, e.g., other comparable or complimentarybrands, for example.

Similar to the brand personality inference system 400 in FIG. 4, thebrand comparison system 700 further obtains as input, via thecrowdsource sources interface 720, crowdsource information from variouscrowdsource sources 725 which may include sources associated with thevarious principle driving factors of brand personality, i.e. sourcesthat provide crowdsource information in the areas of employee imagery,user (customer) imagery, and other marketing message imagery(stakeholder imagery), e.g., marketing messages, and the like. Othermarketing message imagery or stakeholder imagery may include, forexample, brand personality information associated with brand endorsermessages, official announcements, and any marketing messages associatedwith the brand. The particular crowdsource information obtained ispreferably from crowdsource sources 725 associated in some way with thebrands or otherwise reference the brands being compared. For example,the crowdsource information may be obtained from accounts associatedwith the brand on social networking websites, business networkingwebsites, a brand owners own computing systems, trade publicationsources, or any other corpus of natural language information that may beanalyzed using natural language processing techniques to extractinformation indicative of the perception of the brand amongst one ormore groups of people. In the same manner as described above, thecrowdsource information may be categorized into different categories ofcrowdsource information such as employee imagery, user (customer)imagery, and marketing message or other stakeholder imagery.

Group analysis is performed by the group analysis engine 730 on thecrowdsource information using group analysis resources 736. The groupanalysis resources 736 may comprise dictionary-based, image/graphicpattern based, or other resources that are indicative of differentpersonality traits of individuals which can be used as a basis foranalyzing the received crowdsource information, extracting featuresindicative of different personality traits, and associate thosepersonality traits with consumers of brands. In one illustrativeembodiment, the group analysis resources 736 may be similar to the FEresources 450 in FIG. 4, for example.

The group analysis comprises group personality analysis 732 and socialnetwork connections analysis 734. The group personality analysis 732operates to compute personality attribute representations of thepersonalities of the individuals providing crowdsource information abouta brand, e.g., consumers of the brand. The personality attributerepresentations of the consumers may be generated in a similar manner asdescribed above with regard to brands by utilizing models of individualpersonalities having a plurality of personality traits. Tools such asIBM's Personality Insights™, available from International BusinessMachines (IBM) Corporation of Armonk, N.Y., may be used to generate suchpersonality representations of individual consumers.

The personality representations of the individual consumers may beaggregated to generate a global personality representation of consumersof a particular brand. This aggregation may be done with regard to theseparate principle driving factors of brand personality. That is, aseparate personality representation for consumers may be generated foreach of the employee insights, user (customer) insights, and otherstakeholder or marketing message insights. In this way, the aggregatepersonality representation in each category of crowdsource informationis generated to represent a particular type of group of persons.

The social network connections analysis 734 operates to compute therelationship between brands by examining relationships between theconsumers of the brands, e.g., between users (customers, employees, andother stakeholders. For example, a customer of a brand can be thecustomer of another brand. The degree of overlapping customers of two ormore brands, e.g., overlapping Twitter™ followers of two or more brands)is computed to capture the relationship between these two or morebrands. Similarly, employees of a brand owner may have connections withemployees of another brand owner in a professional network site, e.g.,LinkedIn™. The number of connections between consumers is calculated togenerate a metric of the degree of relationship between the two or morebrands.

Thus, for example, in one illustrative embodiment, consumer accountsinformation, which may be part of the crowdsource information receivedvia interface 720, may be analyzed for various crowdsource sources 725to determine which consumers are associated with brand accounts. Forexample, if Brand XY has an account on a social networking website, andBrand ZP has an account on the social networking website, the followersor consumers that have associations within the social networking websiteare identified and the listings of associated consumers/followers arecompared to identify followers/consumers that appear in both listingsfor Brand XY and Brand ZP.

The number of such consumers/followers that are associated with bothbrands is indicative of a degree of overlap of the consumers/followersof the two brands. Various statistical measures may be calculated basedon these raw numbers to determine indicators of brand relationshipsbetween Brand XY and Brand ZP. For example, the ratio of the number ofoverlapping consumers/followers to total unique consumers/followers maybe indicative of the significance of the overlap, e.g., if the ratio isrelatively large, e.g., equal to or greater than a predeterminedthreshold value, then it is indicative of the fact that most consumersof Brand XY are also consumers of Brand ZP, or vice versa. If the ratiois relatively small, e.g., less than a predetermined threshold value,then it is indicative of a gap between the consumers of Brand XY andBrand ZP. This information may be used as a basis for identifyingpotential competitors and partners within a market, e.g., brands that donot have significant overlap are potential competitors with each otherwhile brands that have significant overlap are potential partners. Thisinformation may be used to drive marketing campaigns, communicationssent to consumers, or the like.

It should be appreciated that the group analysis engine 730 performs thegroup personality analysis 732 and social network connections analysis734 with designated comparable or complementary brands 760. That is,groupings of comparable or complementary brands 760 may be pre-generatedand used to identify which brands should be compared with regard to bothgroup analysis and BPS analysis, as performed by the brand comparisonengine 750 described hereafter. Thus, for example, a grouping of brandsXY, ZP, RJ, and CK may be established as brands that are comparable orcomplementary. A comparable brand is one that is associated with a sameor similar product, service, location, concept, or other entity asanother brand. This is often determined in commercial markets asproducts or services that provide a same product/service as anotherbrand, e.g., Ford Motor Company™ is comparable with Chevrolet™ and theApple™ brand is comparable with the IBM™ brand in that both companiesprovide similar products and services. Apple™ may also be comparable toAndroid™ since both companies provide similar wireless phone product,yet IBM™ may not be comparable with Android™. Complementary brands arethose that are associated with entities that are not of the same type,but complement each other. For example, the brand Microsoft™ may becomplementary to Dell™ since Dell™ is a computer manufacturing companyand Microsoft™ is a software manufacturing company.

Thus, by designating groupings of the comparable or complementary brands760, the analysis performed by the group analysis engine 730 and brandcomparison engine 750 may be targeted to the particular groupings andthe brands within those groupings.

The results of the group analysis 730 comprise the group personalityrepresentations of consumers of the brands of a group as well as thesocial network connection statistics of the consumers of the brands ofthat group, e.g., 56% of consumers of brand XY also are consumers ofbrand ZP. This information is output to the brand comparison engine 750for comparison between brands of the group of comparable orcomplementary brands. The brand comparison engine 750 may compare thegroup personality representations of consumers of various brands so asto identify gaps between the personalities of consumers of the variousbrands in the group of comparable/complementary brands. For example, thegroup personality representations may comprise similar personalitytraits and corresponding values associated with these personality traitsas discussed above with regard to brand personalities, or similarpersonality representations may be generated with different personalitytraits that are specific to individuals rather than brands.

In addition to the group analysis engine 730 analysis with regard togroup personality 732 and social network connections 734, the brandcomparison engine 750 further performs comparison of the BPS for thebrands of the specific group so as to identify differences, or gaps,between the various brands. These gaps may be calculated with regard toeach individual brand personality trait, brand personality dimensions,or the like. The degree of difference between the BPS may be calculatedon an individual trait basis or on a general aggregate level, e.g.,Brand XY is considered older than Brand ZP. The differences may benumerical differences of the scores of the corresponding brandpersonality traits of the brands, for example.

In some illustrative embodiments, the BPS of the brands of a grouping ofbrands is used to compute a degree of brand personality singularity forthe group, where brand personality singularity refers to a singular ideaor impression that people have inside their mind about a brand or groupof brands. If a brand's personality is more focused, i.e. there is ahigher degree of singularity, then people are more likely to rememberthat brand and associate the corresponding personality traits with thatbrand. The mechanisms of the illustrative embodiments utilize the BPS ofthe brand to measure the degree of singularity and the stability of thesingularity over time. The brand personality singularity metricsconsider the variances of brand personality scales and the changes ofthese variances over time. The “variance” of a brand personality scaleitself is the variance between the individual brand personality traitsthat make up the brand personality scale. The variance may be calculatedwith regard to groupings of traits, e.g., dividing a brand personalityscale having 42 brand personality traits into three groups of 14 brandpersonality traits each.

For example, a clustering technique may be used to divide thepersonality traits into a number of groups, e.g., groups representingclusters of brand personality traits that have similar brand personalitytrait values. For example, a brand personality scale may have 42 brandpersonality trait ratings which may be clustered into three groups of 14brand personality traits using K-means. The variances between groups ofbrand personality traits and within groups of brand personality traitsmay be used to calculate the degree of singularity, e.g., a ratio of thevariance between the group and other groups, to the variance within thegroup. For example, the variance between groups may be generated bycomputing the mean of each group and then computing the variance betweenthese three groups. The variances within each group may be calculated bycomputing the variance between each pair of members of the group andthen computing the mean of the variances. A higher value ratio, i.e. ahigher singularity value, is indicative of a brand having a strongerassociation of the brand to a corresponding group of brand personalitytraits. It should be appreciated that the singularity is a numericalvalue, but is also correlated with the groups such that each group mayhave a singularity value and a group having a highest singularity valueis indicative of the set of brand personality traits that are mostlikely representative of the brand and remembered by consumers whenpresented with the brand.

Thus, the degree of brand personality singularity can be computed for anindividual brand. It should further be appreciated that the mechanismsof the illustrative embodiments may be utilized to compute the brandpersonality singularity for each brand individually and then comparethese brands based on the brand personality singularity. Moreover, thebrands may be clustered into sets of brands, e.g., brands that arecomparable or complimentary, for which a brand personality singularityof each set of brands may be generated in a similar manner as describedabove but on the entire set of brands. The brand personalitysingularities of the sets of brands may then be compared in a similarmanner such that a comparison of sets of brands may be performed.

With the gap analysis performed by the brand comparison engine 750 basedon the BPS for multiple brands 740 in the grouping of comparable brands760, the brand comparison engine 750 calculates, for each brand, adegree of brand personality singularity. The difference between degreesof brand personality singularity between brands is calculated so as tosee which brands are more focused than others and thus, which brands aremore likely to be remembered by consumers. This information may provideto a brand manager an indication of the relative ranking of brandswithin the grouping of comparable/complementary brands 760 with regardto the strength of consumer perception.

The results of the BPS based comparisons and group analysis may beoutput to the comparison output engine 770 which performed operations togenerate a comparison output 780. The comparison output preferablycomprises a visualization interface that provides various views ofcomparisons between brands of the comparable/complementary brand group760 with regard to brand personality scale gaps 782, identification ofpotential competitors/partners 784, and brand relationships 786. Forexample, dominant traits and the singularity of brand personalities forthe various brands of the group may be visualized, differences betweendegrees of singularity may be visualized, highlighting andidentification of which owners of which other comparable/complementarybrands are potential competitors because of an insignificant amount ofoverlap between the consumers of the brands, highlighting andidentification of which owners of which other comparable/complementarybrands are potential partners because of a significant amount of overlapbetween the consumers of the brands (where significant/insignificant maybe measured based on a comparison to one or more threshold values), andrelationships between the group personality traits of the consumers ofthe various brands. The comparison output 780 may be provided via agraphical user interface (GUI) or other interface that permitsinteraction with the output to obtain various levels of informationincluding drilling down into visualizations to obtain detailedunderlying data, filtering of data, detail-on-demand type interfaces,and the like. Additionally, a time-series visualization technique may beapplied to highlight changes in brand personality perception gaps andbrand personality singularity at specific time periods.

FIG. 7B is an example diagram of a visual output of a brand comparisonsystem that may be part of a graphical user interface in accordance withone illustrative embodiment. As shown in FIG. 7B, each node represents aseparate brand and the size of each node encodes the brand personalitysingularity. That is, larger size nodes have a higher value singularityvalue than smaller size nodes and thus, the larger nodes identify brandpersonality traits that are more representative of the brand personalityfor the brand with regard to the particular grouping of personalitytraits.

In this example, the mechanisms of the illustrative embodiments groupthe personality traits of the BPS into five dimensions, i.e.“Sincerity”, “Excitement”, “Competence”, “Sophistication”, and“Ruggedness.” The visualization shown in FIG. 7B shows the brandsclustered on each dimension with medians of their values with regard toeach of the dimensions shown as vertical lines in each cluster. In thebottom right of the visualization in FIG. 7B, the illustrativeembodiments may compute the cosine similarity ratings between any twobrands based on their BPS values and uses a force-directed graphvisualization technique to visualize the relationship among the brands.If two nodes are close to each other in the bottom right visualization,this means that these two brands have similar personality traits. Itshould be appreciated that FIG. 7B is only an example visualization andmany modifications to the depicted example may be made without departingfrom the spirit and scope of the present invention.

FIG. 8 is a flowchart outlining an example operation for performingbrand comparisons in accordance with one illustrative embodiment. Theoperation outlined in FIG. 8 may be implemented, for example, by thebrand comparison system 700 in FIG. 7A.

As shown in FIG. 8, the operation starts with the receipt of a requestto perform a brand personality comparison of brands in a specified groupof comparable/complementary brands (step 810). The request may bereceived in response to a user, such as a brand manager, initiating therequest via computing system, such as a client computer being used tolog onto the brand comparison engine and utilizing an interface tosubmit a request. Alternatively, the request may be periodicallyscheduled, automatically generated by another process, initiated inresponse to the occurrence of an event, such as an update to a group ofcomparable/complementary brands 760, or the like.

Crowdsource information for the brands in the group of brands to becompared, as identified in the request, is retrieved from one or morecrowdsource sources (step 820). Group analysis is performed based on thecrowdsource information, consumer information providing the crowdsourceinformation, or the like, using group analysis resources (step 830). Thegroup analysis may comprise group personality analysis and/or socialnetwork connections analysis, for example. The results of the groupanalysis are output for comparison by a brand comparison engine (step840). The brand comparison engine identifies brand relationships andpotential competitors and partners based on the group analysis (step850). BPS for the brands are retrieved (step 860) and compared togenerate identification of gaps between brands in the group ofcomparable/complementary brands (step 870). A comparison output isgenerated and output that includes visualizations of the results of thecomparisons (step 880). The operation then terminates.

Thus, in addition to providing mechanisms for generating inferred brandpersonality scales (BPS's) for one or more brands, the illustrativeembodiments further provide mechanisms for comparing brand personalitiesand performing group analysis to provide greater insight into therelative perception of a brand to other brands, such as brandsconsidered to be comparable and/or complementary. In the case ofcomparable or complementary brands, the comparison identifies andhighlights potential competitors and partners of a brand of interest.Moreover, the comparison also identifies the amount of relatedness thatone brand has to the other with regard to cross-brand consumption byusers. Furthermore, differences between the perceived personalities ofbrands may be identified. All of this information is made accessible tousers via a visualization mechanism.

Brand Personality Perception Gap Assessment System

As touched upon above, one analysis that may be performed by themechanisms of the illustrative embodiments is to compare the consumersand BPS of brands which includes identifying gaps between brands. Inaddition, the mechanisms of some of the illustrative embodiments arefurther configured to identify brand perception gaps between theinferred brand personality of a brand and an intended brand personality.In doing so, the mechanisms of the illustrative embodiments are able togenerate outputs indicating the gaps, e.g., numerical differences or afunction, statistical measure, or the like, of such numericaldifferences, between intended and inferred personality of a brand aswell as temporal changes of the inferred personality of the brand. Theoutput may be used as a basis for generating recommendations as toactions (solutions) to be taken to bring the inferred brand personalitymore in line with the intended brand personality, i.e. close the gap orreduce the size of the gap, and, in some cases, initiate performance ofactions by other computing systems to perform operations to modify theinferred brand personality as determined from the crowdsourcing sources.

FIG. 9 is an example block diagram illustrating a brand personalityperception gap assessment system and brand personality perception gaprecommendation system in accordance with one illustrative embodiment.The brand personality perception gap assessment system 900 is designedto help brand manager identify perception gaps of their brands. Theinput to the brand personality perception gap assessment system 900comprises brand perception gaps detection engine 930 which receives asinput an intended brand personality 910 and an inferred brandpersonality scale(s) 920.

The inferred brand personality scale(s) 920 may comprise a historicalset of inferred brand personality scales for the brand such that atemporal analysis of the inferred brand personality scales may beperformed. The temporal analysis may compare successive brandpersonality scales over time to determine trends in the perceived brandpersonality. The trends may be compared to the intended brandpersonality 910 to determine if changes in the perceived brandpersonality are approaching the intended brand personality 910 or aretrending away from the intended brand personality as a whole or on anindividual trait or group of traits (dimension) basis. The trends areindicative of whether brand management operations are successfully beingperformed to achieve the desired perceived brand personality.

As with the gap analysis performed by the brand comparison engine 750 inFIG. 7A, a degree of singularity of brand personality may be calculatedfor both the intended and inferred brand personalities 910 and 920 forcomparison to identify differences. The differences provide a snapshotas to the instant difference between what persons perceive regarding thebrand personality and what the brand manager or owner intends thepersons to perceive. The differences are indicative of a degree, orseverity, of disconnect between the brand owner/manager and the publicwith regard to the brand personality. These differences may berepresented on individual trait basis, groupings of traits, in theaggregate as a global brand personality for the brand, or the like.

For example, the input data 920 may be a set of BPS rating values of onebrand and the corresponding timestamps. The brand perception gapsdetection engine 930 of the brand personality perception gap assessmentsystem 900 may compute the first order and second order derivativesbased on the time series data. The brand perception gaps detectionengine 930 of the system 900 may detect the general trend of increasingor decreasing on these metrics (e.g. BPS ratings) based on a comparisonof current and previous values of these metrics.

The similar time series analysis can also be applied to the principledriving factors of a brand. For example, the brand perception gapsdetection engine 930 may detect the changes of user imagery, employeeimagery, and/or marketing message imagery over time. User imagery,employee imagery, and/or marketing message imagery may be measured byLIWC categories or advanced topic modeling techniques (e.g. LatentDirichlet allocation), as mentioned above.

A gap output 940 may be generated that includes identification of thegaps (942) between the intended and inferred brand personalities 910 and920, e.g., the numerical difference between the inferred brandpersonality trait values and the intended brand personality traitvalues, or a function or statistical measure of such a numericaldifference. Moreover, as noted above, temporal changes of perceivedpersonality 944 are also included in the gap output 940 which includesindications of trends of the perceived personality and whether thetrends are trending towards or away from the intended brand personality910. The gap output 940 may be output as a visualized output of themetrics 942, 944 in a similar manner as the comparison output 780 inFIG. 7A. In addition, the gap output 940 may be provided to a brandpersonality perception gap recommendation system 905.

Brand Personality Perception Gap Recommendation System

As further shown in FIG. 9, the gap output 940 is provided to a gapdiagnosis engine 950 which performs severity analysis 952 andassociation analysis 954. The severity analysis 952 quantifies theseverity of the gap between the inferred and intended brandpersonalities 910 and 920 and further infers possible factors associatedwith these gaps. A severity rating may be computed for each personalitytrait, group of personality traits, or the BPS as a whole, bycalculating numerical differences of the inferred brand personality 920and the intended brand personality 910, on an individual brandpersonality trait basis, a principle driving factor basis, a combinationof these two, or the like, and correlating the numerical difference topre-defined severity ratings. A global severity rating for the gapsbetween the inferred and intended brand personalities 910, 920 may becalculated as a function of the individual severity ratings of the brandpersonality trait gaps and/or principle driving factor gaps. Forexample, an average of the severity ratings of the individual brandpersonality trait gaps may be calculated and used as the global severityrating for gaps associated with the inferred brand personality 920.

The association analysis 954 assesses relevance factors related toperception gaps. The relevance factors may be, for example, theweightings associated with the principle driving factors or individualbrand personality traits provided in the brand personality model asdescribed above. In other illustrative embodiments, the relevance factorrelated to perception gaps may be computed by the lagged correlationbetween the principle driving factor and the brand personality traits.The principle driving factor (e.g., measured by LIWC over time) and thebrand personality trait rating values over time can be viewed as twotime series data sets. The lagged correlation refers to the correlationbetween two time series shifted in time relative to one another. Thehigher the correlation means the factor is more relevant.

The association analysis 954 may comprise the performance of simulationsusing predictive models in the brand personality inference system, forexample, to estimate what principle driving factors are likely to affectthe predicted or inferred brand personality perceptions. The predictivemodels may look at history data for the brand to identify what solutionswere previously performed and the corresponding realized change in thebrand personality traits as a result of the solution being implementedto determine a predicted change for future applications of the solution.The predictive models may also look at the weights of the principledriving factors and/or brand personality traits in the brand personalitytrait model(s) and determine which are likely to affect the predicted orinferred brand personality perceptions the most based on the weightingsin the model(s). Of course, other types of predictive models may also beutilized without departing from the spirit and scope of the presentinvention.

Based on an evaluation of the severity of the gaps between the inferredbrand personality 920 and the intended brand personality 910, as well asthe identification of the principle driving factors for affecting thepredicted or inferred brand personality perceptions, the recommendationengine 960 selects solutions from a solutions knowledge base 980 to berecommended. The solutions in the solutions knowledge base 980 may begood solutions pre-defined by marketing experts. Moreover, therecommendation system 905 may collect or record previous actionsperformed by marketers/managers for this particular brand with theseprevious actions being viewed as candidate solutions.

The selection of a recommended solution may be performed, for example,based on attributes associated with the solutions in the solutionsknowledge base 980. In one illustrative embodiment, each solution in thesolutions knowledge base 980, comprises two attributes: a solutionweight for a principle driving factor, and a solution severity level.The solution weight for a principle driving factor, e.g., a weight valuewithin a numerical range from 0.0 to 1.0. A solution may have effects onthe principle driving factors. For example, obtaining Twitter™ followersmay influence the User Imagery factor, but does not influence theEmployee Imagery factor. Thus, this solution's weight for the UserImagery factor may be relatively higher than its weight for the EmployeeImagery factor, e.g., 1.0 for User Imagery and 0.0 for Employee Imagery.These weight values may be set by marketing experts in the solutionsknowledge base 980 in association with the solution. For example, eachsolution entry in the solutions knowledge base 980 may have the solutioninformation indicating the details of the recommended solution andcorresponding weight values for each of the driving factors.

Moreover, the solution entries in the solution knowledge base 980 mayinclude a solution level value indicative of a severity level of thebrand personality gap for which the solution is suitable. For example,obtaining 100 Twitter™ followers is a low level solution, whileobtaining 10,000 Twitter™ followers is a high level solution. Thus, thesolution level value is set by a marketing expert based on the relativedegree of solution determined by the marketing expert. The solutionlevel is positively correlated to the severity rating such that higherseverity ratings for brand personality gaps correlates with highersolution level.

In some illustrative embodiments, rather than the marketing expertsetting these values, these two solution attributes can be computed fromthe temporal data of a brand by using lagged correlation analysis. Forexample, after the solution “obtaining Twitter™ followers” is performed,the degree of the changes in the factors (e.g. User Imagery) can be usedas solution weights for these factors, e.g., a degree of change in UserImagery BPS value may be used to compute a new weight for the UserImagery factor and an amount of change in the current weight and newweight for the User Imagery factor may be used to compute an relativesolution level, e.g., relatively large changes are indicative of ahigher solution level than relatively smaller changes.

In some illustrative embodiments, the solution entries in the solutionsknowledge base 980 comprise solution templates specifying the brandpersonality trait principle driving factor (e.g., User Imagery, EmployeeImagery, Marketing Message Imagery, or the like) affected by thesolution, a description of the actions to be performed as part of thesolution, an identification of the target of the solution, and anevaluation of the benefit expected to be obtained by the implementationof the solution. The description of the actions to be performed as partof the solution may contain a textual description of what action can betaken, case studies, and/or the like. The identification of the targetof the solution may comprise an actionable link that identifies a targetpopulation to which the action is to be directed or upon which theaction is to be performed. The evaluation of the expected benefit mayalso comprise an actionable link that runs a simulation which indicatesif X % of the target population performed the action, or providedpositive feedback in response to the action being performed on thetarget population, etc., the expected change in brand perception thatwould result.

One example of such a template may be as follows:

Example 1

-   -   Principle Factor Impacted: User Imagery    -   Description of Action: Provide coupons as rewards to existing        Twitter™ followers to refer new customers. According to        psychology theory, people are more likely to redeem coupons if        they score higher in the Big 5 facets Orderliness,        Self-Discipline, and Cautiousness, and lower in Immoderation.    -   Target: Clicking this link will suggest what sub-set of exiting        Twitter™ followers you should give the coupons to. It will        calculate personality portraits for current Twitter™ followers        and provide a subset of people who should be provided rewards.    -   Benefit: A function that shows X % of coupons redeemed and        corresponding addition of Y % of new followers and improved        brand perception of Z % as below:

Brand Perception Coupons Redeemed New Followers Improvement 2% 10 0.10%5% 100 0.20% 10% 500 2.00% 30% 700 5.00% 50% 800 5.50%Similar types of example templates may be generated for other ones ofthe principle driving factors:

Example 2

-   -   Principle Factor Impacted: Employee Imagery    -   Description of Action: Hire employees with above average        stability.    -   Target: Clicking this link will use the LinkedIn™ profiles (or        resumes) of your existing employees to extract skill sets        required for the organization end then identify people with        matching skill sets. Further, this list is filtered to identify        people that have higher percentiles/scores on stability        dimension.    -   Benefit: A function that shows if X % of people join in Y time,        then the brand perception improves by Z %.

Example 3

-   -   Principle Factor Impacted: Marketing Message Imagery    -   Description of Action: Send out messages that show more        cheerfulness.    -   Target: Clicking this link allows brand managers to provide        their press release/marketing message and use IBM's Tone        Analyzer™ service to refine the message to increase cheerfulness        quantification.    -   Benefit: A function that allows brand managers to assess what %        of cheerfulness in what number of messages will lead to        improvement in brand perception.        The brand recommendation engine 960 may compose a combination of        templates for different solutions based on a desired benefit so        as to help a brand manager evaluate the benefits and decide on        the right approach for perception improvement. For example, if        the brand manager seeks to improve brand perception by 2%, the        recommendation engine 960 may output a result that indicates        that to improve brand perception by 2%, use solution template 1        to obtain 100 additional followers on Twitter™ and/or use        solution template 2 to obtain 10 new employees and/or send out 5        marketing messages. The brand manager may then make an informed        decision, based on an overlay of cost and time dimensions on the        above functions, to find an optimal solution for achieving the        desired benefit.

Thus, the recommend solution is customized according to the specificbrand comparison results 970. The recommended solution is output 990 foruse by a brand manager or other authorized user. In one illustrativeembodiment, the output 990 may be a notification of the actions that arerecommended that the brand manager initiate to bring the inferred brandpersonality 920 closer to the intended brand personality 910. In otherillustrative embodiments, the selected and customized solutions mayinclude commands to be sent to other computing systems, applications,and the like, to initiate the actions recommended to improve theinferred brand personality 920 such that it more closely resembles thatintended brand personality 910.

For example, commands may be output as part of the output 990 to othercomputing systems and/or applications to initiate broadcast ofcommunications to consumers. For example, an electronic mail advertisingcampaign or information blanketing campaign may be initiated by causingan electronic mail system to broadcast an electronic mail advertisementto a mailing list via one or more data networks. As another example,commands may be sent to computing systems to print or output coupons fordisbursement to consumers. As another example, commands may be sent tocomputing systems to initiate video or audible output via one or morevideo and/or audible broadcasting systems. A plethora of other actionsmay be initiated based on commands output as part of the output 990depending on the particular desired implementation. Thus, the mechanismsof the illustrative embodiments not only improve the operation of thecomputing systems on which they operate but also perform actions togenerate concrete and tangible results outside the computing systems onwhich the mechanisms of the illustrative embodiments operate.

FIG. 10 is a flowchart outlining an example operation for performingbrand personality perception gap assessment in accordance with oneillustrative embodiment. The operation outlined in FIG. 10 may beimplemented, for example, by the brand personality perception gapassessment system 900 in FIG. 9. As shown in FIG. 10, the operationcomprises receiving an intended brand personality (step 1010) and aninferred brand personality (step 1020). Gaps between the intended andinferred brand personalities are calculated (step 1030) and temporalchanges of the inferred brand personality are calculated (step 1040). Acorresponding output is generated that indicates the brand perceptiongaps and the temporal changes of the perceived personality of the brand(step 1050). The operation then terminates.

FIG. 11 is a flowchart outlining an example operation for performingbrand personality perception gap recommendation and action commandgeneration in accordance with one illustrative embodiment. The operationoutlined in FIG. 11 may be performed, for example, by the brandpersonality perception gap recommendation system 905 in FIG. 9, forexample. As shown in FIG. 11, the operation starts with the receiving ofa brand personality perception gap input (step 1110). This input may bereceived, for example, as gap output 940 from brand personalityperception gap assessment system 900 in FIG. 9. Severity analysis isperformed on the input to determine a severity of the gap(s) indicatedin the input (step 1120). Association analysis is performed on the inputto identify the most relevant factors that affect the brand personalityperception gaps identified in the input (step 1130). Based on therelevant factors and the severity of the gaps, one or more solutions areselected from a solution knowledge base (step 1140) and customized tothe particular brand comparison results of the brand (step 1150). Acustomized solution output is generated (step 1160) which may includethe transmission of commands to other computing systems to cause theother computing systems to perform actions in accordance with a solutionfor brining the inferred brand personality closer to the desired orintended brand personality (step 1170). The operation then terminates.

Thus, the illustrative embodiments provide mechanisms for not onlypredicting or inferring a brand personality based on analysis ofcrowdsource information and comparing brands to determine relationshipsbetween brands, but also provides mechanisms for determining gapsbetween inferred or predicted brand personality and intended or desiredbrand personalities. Moreover, the mechanisms of the illustrativeembodiments further provide for the selection of solutions to bridgethese gaps and even initiate actions through the sending of commands toother computing systems and/or applications to cause operations to beperformed that are likely to bridge these gaps.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprising a processor and a memory, the memory comprising instructions which when executed by the processor cause the processor to implement a brand personality inference engine, the method comprising: receiving, by the brand personality inference engine, crowdsource information, wherein the crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source; extracting, by the brand personality inference engine, features associated with a brand from the crowdsource information; analyzing, by the brand personality inference engine, the features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand; generating, by the brand personality inference engine, an inferred brand personality data structure representing a perceived brand personality of providers providing the crowdsource information; and outputting, by the brand personality inference engine, an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure.
 2. The method of claim 1, further comprising: collecting, from a plurality of users, answers to a survey regarding an association of one or more brand personality traits to the brand; storing the collected answers as a ground truth data structure for training the brand personality model; and training the brand personality model using the ground truth data structure and training crowdsource information collected from one or more crowdsource information sources.
 3. The method of claim 1, wherein the brand personality model comprises a plurality of models, each model associated with a separate brand personality trait of a brand personality scale.
 4. The method of claim 1, wherein each portion in a plurality of portions of the crowdsource information are categorized into different categories based on a type of source from which the portion of the crowdsource is obtained, wherein each category is associated with a different principle driving factor of brand personality trait perception.
 5. The method of claim 4, wherein generating the brand personality model comprises calculating, for each combination of brand personality trait and category, a weight value indicating a degree of importance of the category to inferring the brand personality trait.
 6. The method of claim 4, wherein the categories comprise a User Imagery category, an Employee Imagery category, and a Marketing Messages Imagery category.
 7. The method of claim 1, wherein analyzing the features associated with the brand comprises: extracting features from the crowdsource information; comparing the extracted features to brand personality trait features associated with brand personality traits of a brand personality scale to determine matches between the extracted features and the brand personality trait features; and calculating, for each brand personality trait, a value for each brand personality trait feature based on an amount of matching of the extracted features to the brand personality trait features associated with the brand personality trait.
 8. The method of claim 7, wherein the features extracted from the crowdsource information comprises natural language terms, natural language phrases, topics, or patterns of content indicative of a brand personality trait of a brand, and wherein the brand personality trait features are keywords, key phrases, topics, or patterns of content previously associated with a particular brand personality trait such that each brand personality trait has its own associate set of brand personality trait features.
 9. The method of claim 1, wherein outputting the output indicating aspects of the perceived brand personality further comprises: generating a qualitative explanation of the aspects of the perceived brand personality based on user feedback; and outputting the qualitative explanation as part of the output.
 10. The method of claim 1, wherein the crowdsource information source is at least one of a social networking website, a business networking website, a brand owner's computing system, or a trade publication source computing system, and wherein the crowdsource information comprises at least one of instant messages, textual postings to websites, electronic articles in an electronic journal or publication, electronic mail messages, or webpage content.
 11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to implement a brand personality inference engine that operates to: receive crowdsource information, wherein the crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source; extract features associated with a brand from the crowdsource information; analyze the features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand; generate an inferred brand personality data structure representing a perceived brand personality of providers providing the crowdsource information; and output an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure.
 12. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to: collect, from a plurality of users, answers to a survey regarding an association of one or more brand personality traits to the brand; store the collected answers as a ground truth data structure for training the brand personality model; and train the brand personality model using the ground truth data structure and training crowdsource information collected from one or more crowdsource information sources.
 13. The computer program product of claim 11, wherein the brand personality model comprises a plurality of models, each model associated with a separate brand personality trait of a brand personality scale.
 14. The computer program product of claim 11, wherein each portion in a plurality of portions of the crowdsource information are categorized into different categories based on a type of source from which the portion of the crowdsource is obtained, wherein each category is associated with a different principle driving factor of brand personality trait perception.
 15. The computer program product of claim 14, wherein generating the brand personality model comprises calculating, for each combination of brand personality trait and category, a weight value indicating a degree of importance of the category to inferring the brand personality trait.
 16. The computer program product of claim 14, wherein the categories comprise a User Imagery category, an Employee Imagery category, and a Marketing Messages Imagery category.
 17. The computer program product of claim 11, wherein analyzing the features associated with the brand comprises: extracting features from the crowdsource information; comparing the extracted features to brand personality trait features associated with brand personality traits of a brand personality scale to determine matches between the extracted features and the brand personality trait features; and calculating, for each brand personality trait, a value for each brand personality trait feature based on an amount of matching of the extracted features to the brand personality trait features associated with the brand personality trait.
 18. The computer program product of claim 17, wherein the features extracted from the crowdsource information comprises natural language terms, natural language phrases, topics, or patterns of content indicative of a brand personality trait of a brand, and wherein the brand personality trait features are keywords, key phrases, topics, or patterns of content previously associated with a particular brand personality trait such that each brand personality trait has its own associate set of brand personality trait features.
 19. The computer program product of claim 11, wherein outputting the output indicating aspects of the perceived brand personality further comprises: generating a qualitative explanation of the aspects of the perceived brand personality based on user feedback; and outputting the qualitative explanation as part of the output.
 20. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: receive crowdsource information, wherein the crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source; extract features associated with a brand from the crowdsource information; analyze the features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand; generate an inferred brand personality data structure representing a perceived brand personality of providers providing the crowdsource information; and output an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure. 